What are the key steps to framing business problems?

You probably need to start with a "Translation" step and its go beyong than just getting the context. Be sure you are understanding the business and stakeholders needs (translate business needs to data needs), validating the opportunities with data teams (translate data needs to tech opportunities) and then again with business to validate the project proposal (translate tech & data opportunities to the business). Keep this in mind before fit your project in a data pipeline and avoid reworks.

Framing business problems effectively involves identifying the core issue, understanding the context, and establishing clear, measurable objectives. Key steps include gathering relevant data, engaging stakeholders to gain diverse perspectives, defining the problem statement in a concise manner, setting specific, achievable goals, and prioritizing issues based on impact and feasibility. This structured approach ensures a focused direction for developing solutions and allocating resources efficiently.

Understanding the context involves gaining insight into the surrounding circumstances, factors, and conditions that influence a situation or problem. It requires considering the broader environment, stakeholders, goals, and constraints to inform decision-making and problem-solving. For example, in healthcare analytics, understanding the context may involve analyzing patient demographics, medical history, and treatment protocols to develop personalized care plans tailored to individual needs while considering resource constraints and regulatory requirements. This comprehensive understanding ensures that solutions are relevant, effective, and aligned with the overarching objectives.

Framing business problems involves identifying key issues, defining objectives, and formulating clear questions. Key steps include understanding stakeholder needs, gathering relevant data, analyzing root causes, considering constraints, and ensuring alignment with organizational goals. Effective problem framing sets the foundation for targeted solutions and informed decision-making.

Understanding the context of a business problem is most important steps of framing business problem. As a data scientist or data science manager, one should go that extra mile to get in deep of the context .Need to have a set of questions on who are the stakeholders, beneficiaries, What are the problem or challenges they are facing , how is it executed currently ? who all are affected by this problem? what are the potential benefits that stakeholders are expecting by solving this problem? by answering these questions , one will get to know about the context

It's important to ask relevant questions that provide clarity and context. Here are some key questions to consider: - What are the goals and objectives we want to achieve by solving this problem? - Who are the stakeholders involved, and what are their perspectives and expectations? - What are the key metrics or performance indicators that will be impacted by solving this problem? - What data or information do we need to analyze to gain insights into this problem? - What are the potential constraints, limitations, or dependencies that need to be considered? By asking these questions and engaging in discussions with relevant stakeholders, you can define a problem statement that encapsulates the challenges, objectives, and expectations.

I remember asking a Fortune 50 client why they were embarking on a major project. "To increase revenue and reduce expenses" was the answer. A lot of goal defining is waste of time. I especially think this is true for data initiatives. Scientists don't set goals before they examine a data set. The best insights come from having an open mind. Being too outcomes driven closes us off to possibilities. Once you see an insight in the data, then create a hypothesis and test it.

The problem statement succinctly defines the specific issue or opportunity that needs to be addressed, providing clarity and focus for problem-solving efforts. It outlines the current situation, the desired outcome, and any constraints or considerations that must be taken into account. For example, in a retail setting, a problem statement could be: "To reduce customer churn and improve retention rates among online shoppers by identifying key factors influencing customer attrition and implementing targeted strategies to increase customer loyalty. Constraints include limited budget for incentives and the need to maintain a seamless online shopping experience."

Before jumping into solving a problem, one should define the problem statement .Defining a problem statement means defining what is the scope of this project ? what is not ? and answering the WWWH questions like who are the stakeholders? who is funding project? who is benefitted by solution or who is most impacted by the situation ? Why is it important to solve this problem? What is problem and how you will measure is as done or success? Do you have any framework , goal or target to measure the success . Can you quantify the results/outcomes of the solution? all these need to be thought through as part of defining the problem statement

1. Defining the problem statement is the first step in any data science project. 2. It involves understanding the business problem and identifying the specific question that needs to be answered through data analysis. 3. For example, a company may want to know which marketing campaign is most effective in driving sales. 4. The problem statement in this case would be: "Identify the marketing campaign that generates the highest ROI." 5. Defining the problem statement helps ensure that the data analysis is focused and relevant to the business goals.

I have learned (the hard way!) To rethink my assumptions as my starting point. Thinks you believe to be true without yet having proof. Questioning these beliefs, even “obvious” ones is very important. I have personally experienced delays and setbacks for making assumptions about data or software that later turned to be grounded in old thinking or no longer applicable thinking. Constraints, on the other hand, are the boundaries of your project. They define the scope of your creativity and problem-solving. By acknowledging them early you ensure solutions are not just innovative but also practical and feasible within the given limitations. This step is about balancing ambition with reality, ensuring your solutions are grounded yet visionary.

Would like to use my favourite quote of making obvious more obvious in this scenario.We should clearly define the assumptions that we are making while solving the problem and also we need to communicate it clearly to all stakeholders to avoid anykind of mis communication later. You also need to be aware of constraints that you have in this project, be it time related or cost related , you need to be mindful about it . For eg in data scenario, many problem statements need high accuracy solution , where as in some areas they are ready to tradeoff the accuracy for compute power or time or even for explainability of models. So you need to have clear idea about the assumptions and constraints which will help you to prioritize and de prioritize

Identifying assumptions involves recognizing underlying beliefs or conditions that are taken for granted but may impact the problem-solving process. Constraints, on the other hand, are limitations or restrictions that must be considered when developing solutions. For instance, in developing a marketing strategy, an assumption might be that customer preferences remain consistent, while constraints could include budgetary limitations or regulatory requirements. Recognizing and addressing these factors early in the problem-solving process helps ensure more effective and realistic solutions.

Acknowledge and document the assumptions and constraints that might impact your analysis and solutions. This step helps in managing expectations and avoiding pitfalls that arise from overlooking crucial factors.

Identifying assumptions and constraints is key for realistic problem-solving ???♂???: - Assumptions: Hypotheses you consider true for the analysis. Validate them to avoid bias. - Constraints: Limitations affecting the project, such as resources or deadlines. This step helps in: - Testing Validity: Assessing how assumptions hold up against real data. - Risk Mitigation: Adjusting strategies to navigate constraints. - Transparent Communication: Keeping stakeholders informed of potential impacts. Understanding these elements guides effective, feasible solutions within project boundaries.

Selecting data sources and methods involves identifying the most relevant sources of data and determining the appropriate techniques for collecting, processing, and analyzing that data to address the problem at hand. This may include structured data from databases, unstructured data from text sources, or even external data from APIs or web scraping. For example, a retail company aiming to optimize its inventory management might combine sales data from their POS system with weather data to forecast demand using time series analysis and machine learning algorithms. The key is to choose sources and methods that provide actionable insights and support informed decision-making.

Selecionar as ferramentas adequadas tem como objetivo que você obtenha insights precisos para tomar decis?es estratégicas e eficazes, para isso ao escolher as fontes de dados devemos considerar a relevancia realizar a verifica??o da confiabilidade e acessibilidade. Importante lembrar que n?o existe uma solu??o única para todos os problemas, a escolha das ferramentas deve ser feita caso a caso e consultar especialistas em análise de dados pode ser útil para tomar decis?es informadas. Ao investir tempo e aten??o na escolha das fontes de dados e dos métodos de análise, você estará abrindo caminho para o sucesso na resolu??o de problemas de negócios e na conquista de seus objetivos.

Select appropriate data sources and analytical methods that align with your problem statement and objectives. Consider the quality, reliability, and relevance of the data, as well as the suitability of the chosen methods for extracting insights.

Selecting data sources and methods is crucial for solving business problems effectively ????: - Data Sources: Determine where to gather data, ensuring relevance and quality. - Methods: Pick techniques suited to your data and goals, from cleaning to modeling. Key considerations: - Problem Statement Alignment: Ensure choices directly address the problem. - Assumptions and Constraints: Adapt methods to fit project limitations. - Feasibility and Validity: Confirm data and methods are practical and sound. This strategy sets a clear path for your data science process, maximizing outcome accuracy and relevance.

Determine the data sources and information sources that are relevant to the problem at hand, including internal data repositories, external databases, market research reports, and expert opinions. Select appropriate data collection methods, research methodologies, and analytical techniques that can provide insights into the problem and support decision-making.

?? Model Evaluation Metrics: It is important to measure and quantify model performance before selecting the best predictive analytics model. First you need to define a Success Criteria to evaluate predictive analytics models, depending on the type of problem. - - Regression problems: Metrics like mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), or coefficient of determination (R-squared) can be used. -- Classification problems: It can be Accuracy Score, precision, recall, F1-score, AUC-ROC, etc. -- Clustering problems: where the output is a group of similar data points, metrics like silhouette score, etc. Remember to Choose metrics based on business statement, data sources and business goal.??

As a seasoned Data Scientist, I find that framing business problems boils down to three key steps: firstly, understanding the problem thoroughly by gathering insights from stakeholders; secondly, identifying relevant data sources and methods to tackle the issue effectively; and finally, defining clear metrics and outcomes to measure success. It's crucial to bridge the gap between technical solutions and real-world impact, ensuring alignment with stakeholders' needs and demonstrating tangible value. This approach not only drives actionable insights but also fosters trust and collaboration within the business ecosystem.

Determine the key metrics that will be used to measure the success of your solution. These metrics should be aligned with the problem statement and should reflect the desired outcomes. Establishing clear metrics ensures that your efforts are focused on achieving tangible results.

Be prepared for the stakeholders to change what they really want to know as the project evolves - as new data and discoveries arise, they might get new ideas. In which case, go through the same steps of framing the new questions, decide on KPIs, etc. But beware of scope creep! Ask: Will current resources and timeline support the modified query? Might have to further iterate the new query to fit project constraints.

Clearly defining the metrics and success criteria is crucial for evaluating the effectiveness of your data science solution. Discuss with stakeholders to identify the key performance indicators (KPIs) that directly align with the business objective. This step ensures that your analysis and models are built to deliver measurable outcomes and provide value to the company.

In addition to the key steps mentioned above, there are several other factors to consider when framing business problems. These include stakeholder engagement, ethical considerations, scalability of solutions, and potential risks. By addressing these factors, you can enhance the effectiveness of your problem-solving approach.

Gather Stakeholder Input: Involve relevant stakeholders in the process of framing the business problem to gain diverse perspectives and ensure alignment with organizational goals and priorities. Conduct Root Cause Analysis: Dig deeper into the problem to identify underlying causes and contributing factors. This involves asking "why" multiple times to uncover the root cause of the issue.

To effectively frame business problems, clearly define the issue, its context, and business impact. Identify key stakeholders and their needs. Collect relevant data to understand the problem's scope and factors. Define specific, measurable goals for the solution. Brainstorm and evaluate potential solutions against criteria. Develop a detailed action plan with steps, resources, and timelines. Implement, monitor progress, and collect feedback. Finally, measure results against objectives and adjust as needed.